论文： GPU Kernels for Block-SparseWeights 论文链接：https://s3-us-west-2.amazonaws.com/openai-assets/blocksparse/blocksparsepaper.pdf 摘要： 我们正在发布一个低级别神经网络架构（带有块稀疏（block-sparse）权重）的高度优化 GPU 内核，它允许在权重矩阵中带有灵活可配置的块稀疏性模式的线性层（包括卷积...
近日,OpenAI 在其一篇题为《Block-Sparse GPU Kernels》的博文中发布了一个低级别神经网络架构的高度优化 GPU 内核,并且这个神经网络带有「块稀疏」(block-sparse)权重。根据已选的稀疏性,这些内核可以比 cuBLAS 或 cuSPARSE 运行快几个数量级,并在文本情感分析与文本、图像生成建模方面取得了当前最优结果。机器之心...
We’re releasing highly-optimized GPU kernels for an underexplored class of neural network architectures: networks with block-sparse weights. Depending on the chosen sparsity, these kernels can run orders of magnitude faster than cuBLAS or cuSPARSE. We’
The blocksparse package contains TensorFlow Ops and corresponding GPU kernels for block-sparse matrix multiplication. Also included are related ops like edge bias, sparse weight norm and layer norm. To learn more, see the launch post on the OpenAI blog. Prerequisites First, you need at least one...
nmSPARSE, a library of efficient GPU kernels for two fundamental operations in neural networks with N:M sparse weights: sparse matrix-vector multiplication (SpMV) and sparse matrix-matrix multiplication (SpMM). By exploiting the intrinsic balance characteristic of N:M sparsity...
sparse matrix multiplication object bsmm = BlocksparseMatMul(sparsity, block_size=block_size) # Input to graph x = tf.placeholder(tf.float32, shape=[None, hidden_size]) # Initialize block-sparse weights w = tf.get_variable("w", bsmm.w_shape, dtype=tf.float32) # Block-sparse matrix ...
gather_16bit_weights_on_model_save=False use_all_reduce_for_fetch_params=False stage3_gather_fp16_weights_on_model_save=False ignore_unused_parameters=True legacy_stage1=False round_robin_gradients=True zero_hpz_partition_size=1 zero_quantized_weights=False zero_quantized_nontrainable_weights=...
SPARSE_WEIGHTS_DECOMPRESSION_RATE: 1.0 [ INFO ] DYNAMIC_QUANTIZATION_GROUP_SIZE: 32 [ INFO ] KV_CACHE_PRECISION: <Type: 'float16'> [ INFO ] AFFINITY: Affinity.CORE [ INFO ] [ INFO ] GPU : [ INFO ] SUPPORTED_PROPERTIES: [ INFO ...
[root@server3 MLNX_OFED_LINUX-4.7-3.2.9.0-rhel7.6-x86_64]# cat .supported_kernels 3.10.0-957.el7.x86_64 注:由以上可知下载的默认驱动支持当前的内核版本如果当前内核与支持内核不匹配,手动编译适合内核的驱动,在编译之前首先安装gcc编译环境和kernel开发包 [root@server3 MLNX_OFED_LINUX-4.7-3.2.9.0-...
In addition, Ampere architecture GPUs introduce hardware support for processing matrices with specific sparsity patterns at up to 2x throughput, by skipping the zero-valued elements. In the GA10x configuration, each SM has double the throughput of a Turing SM when processing sparse matrices, while ...